class_docstr: Augmenter that leverage semantic meaning to substitute word.
  
  :param str lang: Language of your text. Default value is 'eng'.
  :param float aug_p: Percentage of word will be augmented.
  :param int aug_min: Minimum number of word will be augmented.
  :param int aug_max: Maximum number of word will be augmented. If None is passed, number of augmentation is
      calculated via aup_p. If calculated result from aug_p is smaller than aug_max, will use calculated result from
      aug_p. Otherwise, using aug_max.
  :param list stopwords: List of words which will be skipped from augment operation.
  :param str stopwords_regex: Regular expression for matching words which will be skipped from augment operation.
  :param func tokenizer: Customize tokenization process
  :param func reverse_tokenizer: Customize reverse of tokenization process
  :param str name: Name of this augmenter
  
  >>> import nlpaug.augmenter.word as naw
  >>> aug = naw.AntonymAug()
code: def get_candidates(self, tokens, token_idx):
      original_token = tokens[token_idx][0]
      word_poses = PartOfSpeech.constituent2pos(tokens[token_idx][1])
      candidates = []
      if word_poses is None or len(word_poses) == 0:
          # Use every possible words as the mapping does not defined correctly
          candidates.extend(self.model.predict(tokens[token_idx][0]))
      else:
          for word_pos in word_poses:
              candidates.extend(
                  self.model.predict(tokens[token_idx][0], pos=word_pos)
              )
  
      candidates = [
          c for c in candidates if c.lower() != original_token.lower()
      ]
      return candidates
  
file_path: nlpaug/nlpaug/augmenter/word/antonym.py
imports: from nlpaug.augmenter.word import WordAugmenter
  from nlpaug.util import Action, Doc, PartOfSpeech, WarningException, WarningName, WarningCode, WarningMessage
  import nlpaug.model.word_dict
label_desc: Use image node_id 4 for calling the AntonymAug obj's underlying member method code with example usage: obj.get_candidates(tokens, token_idx) and returns: candidates